Stock/test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "from typing import Dict\n",
    "from render import * \n",
    "import db as database\n",
    "from jinja2 import Environment, PackageLoader, select_autoescape\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'db' from 'c:\\\\Users\\\\Monoid\\\\Desktop\\\\stock\\\\db.py'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importlib.reload(database)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = sqlite3.connect(\"stock.db\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "krx = database.GetAllKRXCorp(db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "krxDf = pd.DataFrame([corp.toDict() for corp in krx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = GetStockPriceFrom(db,\"155660\", 61)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = pd.DataFrame(data, columns=[s for s in database.STOCK_INDEX.__members__.keys()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "s.set_index(\"DATE\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock = s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "d5 = stock[\"CLOSE\"].loc[::-1].rolling(window=5\n",
    "        ).mean().dropna().loc[::-1]\n",
    "d20 = stock[\"CLOSE\"].loc[::-1].rolling(window=20\n",
    "        ).mean().dropna().loc[::-1]\n",
    "d60 = stock[\"CLOSE\"].loc[::-1].rolling(window=60\n",
    "        ).mean().dropna().loc[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "ewm12 =  stock[\"CLOSE\"].loc[::-1].ewm(span=12).mean().loc[::-1]\n",
    "ewm26 =  stock[\"CLOSE\"].loc[::-1].ewm(span=26).mean().loc[::-1]\n",
    "macd = (ewm12 - ewm26)\n",
    "signal = macd.ewm(span=9).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DATE\n",
       "2022-05-20    148.895069\n",
       "2022-05-19    152.584580\n",
       "2022-05-18    122.762721\n",
       "2022-05-17     97.031260\n",
       "2022-05-16     50.671176\n",
       "                 ...    \n",
       "2022-02-28      7.956286\n",
       "2022-02-25      1.291958\n",
       "2022-02-24     -0.770309\n",
       "2022-02-23      4.262821\n",
       "2022-02-22      0.000000\n",
       "Name: CLOSE, Length: 61, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2022-05-16', '2022-05-15']"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
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       "   a  b  c\n",
       "0  1  2  3\n",
       "1  4  5  6"
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       "Empty DataFrame\n",
       "Columns: [a, b, c]\n",
       "Index: []"
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     "execution_count": 132,
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